本文關(guān)鍵詞： 腦電信號 癲癇發(fā)作 分形特征 微分盒維 毯子覆蓋技術(shù) 稀疏表示 核函數(shù)技術(shù) 協(xié)作表示　出處：《山東大學(xué)》2014年博士論文　論文類型：學(xué)位論文

【摘要】：癲癇發(fā)作是腦內(nèi)神經(jīng)元陣發(fā)性異常超同步化電活動的臨床表現(xiàn),具有反復(fù)性、突發(fā)性和暫時性等特點。作為研究癲癇發(fā)作特征的重要工具,腦電圖所反映的發(fā)作信息是其他生理學(xué)方法所不能提供的。利用信號處理技術(shù)和模式識別方法自動檢測癲癇腦電信號,對于減輕醫(yī)生負擔(dān)并提高癲癇的診斷效率具有重要意義。 目前,在腦電信號的分析研究中,非線性動力學(xué)的應(yīng)用為癲癇腦電的識別提供了更加豐富的重要信息,但是多數(shù)非線性腦電特征具有較復(fù)雜的計算過程,無法保證檢測算法的實時性。同時,傳統(tǒng)的“腦電特征提取+分類器”的自動檢測方法會提取多個腦電特征,然后組成特征向量或進行特征選擇,這樣進一步加劇了算法的計算復(fù)雜度,并且增加了特征選取的難題。本文立足于腦電信號的特征提取、分類識別和癲癇發(fā)作的自動檢測的研究,圍繞腦電信號的非線性特征提取、分形特性以及基于稀疏表示的腦電分類等內(nèi)容展開以下研究： 首先,本文將非線性動力學(xué)的重要分支——分形幾何理論應(yīng)用到腦電信號的分析與處理中。將常用于圖像分形計算的微分盒維算法引入到一維腦電信號的分形分析中,計算了腦電信號的盒維數(shù)及其分形截距,并發(fā)現(xiàn)與盒維數(shù)相比,其分形截距能夠更好的區(qū)分癲癇發(fā)作期和間歇期的腦電。之后,本文又通過改進毯子覆蓋技術(shù)計算出腦電信號的多尺度毯子維及其分形截距,并發(fā)現(xiàn)在不同尺度上它們在臨近癲癇發(fā)作前均會出現(xiàn)明顯變化。 其次,本文基于所提出的腦電分形特征進一步提出了癲癇發(fā)作檢測與預(yù)測方法。將腦電信號的微分盒維的分形截距作為其非線性特征,然后結(jié)合極端學(xué)習(xí)機(ELM)分類器,提出了一種適于多導(dǎo)長程腦電的癲癇發(fā)作檢測方法。采用BLDA算法對腦電的多尺度毯子維及其分形截距在發(fā)作前期的變化進行檢測,從而實現(xiàn)了對癲癇發(fā)作的預(yù)報。實驗驗證的結(jié)果不僅說明了本文所提出的腦電分形特征的有效性,而且體現(xiàn)了所提出的檢測和預(yù)測方法的良好性能。 再次,本文依據(jù)稀疏表示分類方法,提出了一種基于Kernel稀疏表示的癲癇腦電識別算法。在該方法框架中,先通過求解最小l1范數(shù)優(yōu)化問題求得待測腦電在腦電訓(xùn)練集上的稀疏表示系數(shù),然后,分別計算發(fā)作期訓(xùn)練樣本和間歇期訓(xùn)練樣本對待測腦電的稀疏表示重構(gòu)誤差,通過比較誤差的大小來確定待測腦電的類別。與常見的“腦電特征提取+分類器”的腦電分類方法不同,基于稀疏表示的腦電識別方法避免了腦電特征提取和選擇的問題,更加完整地保留了腦電信號所攜帶的信息。為了進一步提高識別效果,本文將核函數(shù)技術(shù)與稀疏表示分類方法相結(jié)合,通過預(yù)先增強腦電樣本的可分性來進一步提高對癲癇腦電的識別率。實驗結(jié)果表明,基于Kernel稀疏表示的腦電分類方法取得了更加理想的分類性能。 最后,在基于稀疏表示的癲癇腦電識別方法的基礎(chǔ)上,進一步將計算待測腦電稀疏表示系數(shù)過程中所利用的最小l1范數(shù)優(yōu)化問題替換為最小l2范數(shù)優(yōu)化問題,從而可以通過正則化最小二乘算法(Regularized Least Square, RLS)解析地求得待測腦電的稀疏系數(shù),避免了復(fù)雜的迭代運算,大大降低了算法的復(fù)雜性。由于改進后的方法強調(diào)來自所有類別的訓(xùn)練樣本對測試樣本的協(xié)作表示所起到的關(guān)鍵作用,因此稱為協(xié)作表示分類方法。同樣,本文將核函數(shù)技術(shù)與協(xié)作表示分類方法相結(jié)合,并且將兩類腦電訓(xùn)練樣本所對應(yīng)的重構(gòu)誤差相減,所得的差值作為輸出的決策變量,從而引入了平滑濾波等后處理環(huán)節(jié),提出了較為完善的基于Kernel協(xié)作表示的癲癇發(fā)作檢測方法。利用連續(xù)長程腦電數(shù)據(jù)對該方法的性能進行評價,實驗發(fā)現(xiàn),所提出的檢測方法不但取得了較理想的檢測結(jié)果,而且其較快的運算速度基本符合實時在線的發(fā)作檢測的需求。 本文的研究工作將有助于進一步推動癲癇自動檢測在技術(shù)理論、算法和臨床應(yīng)用方面的研究,對于腦電信號的非線性特征提取、分形理論在腦電分析中的應(yīng)用以及腦電信號的稀疏表示分類方法起到了積極的推進作用。由于實驗所用腦電數(shù)據(jù)的局限性,本文所提出的幾種癲癇腦電識別和自動發(fā)作檢測方法還需要更大量的臨床腦電數(shù)據(jù)來進一步驗證它們的性能。
[Abstract]:A seizure is a clinical manifestation of brain neurons abnormal paroxysmal synchronized electrical activity has repeatedly, sudden and temporary. As an important tool of epilepsy, EEG information reflects the attack is methodology can provide other physiological. Automatic detection of epileptic EEG signal processing and utilization the pattern recognition method to reduce the burden on doctors and has important significance to improve the efficiency of diagnosis of epilepsy.
At present, the research on analysis of EEG signals, provides important information more abundant application of nonlinear dynamics for the identification of epileptic EEG, but most of the nonlinear characteristics of EEG with the calculation process is complicated, the real-time detection algorithm can not be guaranteed. At the same time, the traditional "EEG feature extraction + classifier" automatically detection method can extract multiple EEG features, then feature vector or feature selection, which further exacerbated the computational complexity of the algorithm, and increase the problem of feature selection and feature extraction. Based on the EEG signal, the automatic detection of the recognition and classification of seizures, nonlinear feature extraction on EEG signal, fractal characteristics and sparse representation based classification of EEG content following research:
First of all, this will be an important branch of nonlinear dynamics, fractal geometry theory is applied to the analysis and processing of EEG signals. The fractal analysis of differential box counting algorithm is commonly used in the calculation of fractal image into one-dimensional EEG, EEG signal box dimension and fractal intercept were calculated, and compared with the box the dimension of the EEG and intermittent period between the epilepsy better fractal intercept attack. Later, this paper improved blanket technology to calculate the multi-scale blanket dimension EEG and fractal intercept, and find the different scale of them near the seizure before there will be significant changes.
Secondly, the fractal characteristics of EEG based on the proposed detection and prediction of seizures. The fractal intercept differential box dimension of EEG signal as its nonlinear characteristics, and then combined with the extreme learning machine (ELM) classifier is proposed, which is suitable for the long time EEG seizure detection method. The BLDA algorithm is used for EEG multiscale blanket dimension and its fractal intercept were detected in the early attack changes, so as to achieve seizure prediction. Experimental results not only illustrate the effectiveness of the fractal characteristics of EEG in this paper, but also reflects the good performance of detection and prediction of the proposed method.
Again, based on the sparse representation classification method, this paper proposes a new Kernel based on sparse epileptic EEG recognition algorithm. In this method framework, first by solving the minimum L1 norm optimization problem to obtain the measured EEG EEG in the sparse representation coefficient, the training set is then calculated respectively the training sample and attack the intermittent period of training samples sparse EEG to said reconstruction error, by comparing the size of the error to determine the type of EEG measured. With the usual "EEG feature extraction + Classifier" EEG classification methods, EEG recognition method based on sparse representation avoids the problem of feature selection and extraction of brain power more, to retain the integrity of the EEG information carried by. In order to further improve the recognition effect, the kernel function and the sparse combination classification method, through the pre enhanced EEG samples can be divided into It further improves the recognition rate of epileptic EEG. Experimental results show that the EEG classification method based on Kernel sparse representation achieves a better classification performance.
Finally, on the basis of epileptic EEG recognition method based on sparse representation on further calculating EEG sparse representation of the minimum L1 norm optimization problem with minimum L2 norm optimization problem using coefficient process can be obtained by regularized least squares algorithm (Regularized Least Square, RLS) to obtain the sparse coefficient of EEG the measured analytically, avoid the complex iterative operation, greatly reduces the complexity of the algorithm. The improved method emphasizes collaboration from all categories of training samples of the test sample said the key role played by the so called collaborative representation classification method. Also, the kernel technology and collaboration said according to the classification method, and the corresponding two kinds of EEG training sample reconstruction error subtraction, the difference of output as the decision variables, then the smoothing filter at Physical link detection method is proposed based on Kernel collaboration said relatively perfect seizures. To evaluate the performance of the continuous long Cheng Nao electricity data of the experiment found that the method not only achieved the ideal results, but its fast basically meets the real-time online attack detection needs.
This research work will help to further promote the automatic detection of epilepsy in theory, algorithm research and clinical application of the nonlinear feature extraction for EEG signal, sparse fractal theory in application to the analysis of EEG and EEG signal classification method that has played a positive role in promoting. Due to the limitation of the brain the data used in the experiment, this paper proposed several kinds of epileptic EEG recognition and automatic seizure detection methods still need more clinical EEG data to verify their performance.

【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TN911.7

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